kvr2 0.2.0

New Features

• Added model_info() function to extract metadata used for calculations, such as regression type (linear/power), sample size (\(n\)), and degrees of freedom (\(k\), \(df_{res}\)).
• The print() methods for r2_kvr2 and comp_kvr2 objects now display model information at the end of the output by default.
• Added a new argument model_info (default is TRUE) to print() methods, allowing users to toggle the display of model metadata.
• Added comp_model() to contrast intercept and no-intercept versions of the same model using QR-decomposition for robust re-calculation.
• Added a set of plot functions that visually display the difference between the actual and predicted values of the dependent variable in the model and the coefficient of determination.

Improvements

• Improved the auto-detection logic for power regression models. It now correctly distinguishes between a variable named “log” and the log() function call (e.g., lm(log(y) ~ x) is correctly identified while lm(log ~ x) is treated as linear).
• Internal calculations now explicitly store model attributes to ensure consistency between r2() and model_info().

Bug Fixes

• Fixed several typographical errors in the output and documentation. Notably, corrected “RMES” to “RMSE” (Root Mean Square Error) in the output of comp_fit().
• Fixed a misclassification issue where models with a dependent variable named “log” were incorrectly identified as power regression when using type = "auto".
• Corrected “liner” to “linear” in various internal labels and documentation to ensure consistency with standard statistical terminology.

kvr2 0.1.0

• First releases on CRAN.